Version: 5.3

Expressions, Functions and Operators

Hazelcast supports a subset of standard SQL expressions, functions, and operators as well as non-standard functions for special cases such as windowed aggregation.

For information about input and return data types, see Data Types.

Operators

Operators are used to evaluate and/or compare one or more operands. For example, in the expression (X + Y), the + operator evaluates the addition of the X and Y operands.

Hazelcast supports the following SQL operators.

Logical Operators

Hazelcast supports the AND, OR, and NOT logical operators. Logical operators allow only BOOL or NULL data types as input. The result can be TRUE, FALSE, or NULL:

X	Y	X AND Y	X OR Y
`TRUE`	`TRUE`	`TRUE`	`TRUE`
`TRUE`	`FALSE`	`FALSE`	`TRUE`
`TRUE`	`NULL`	`NULL`	`TRUE`
`FALSE`	`FALSE`	`FALSE`	`FALSE`
`FALSE`	`NULL`	`FALSE`	`NULL`
`NULL`	`NULL`	`NULL`	`NULL`

X	NOT Y
`TRUE`	`FALSE`
`FALSE`	`TRUE`
`NULL`	`NULL`

Comparison Operators

Comparison operators compare two or more operands, and always return a BOOL. Comparisons require operands to be of comparable type.

Operator name	Syntax	Description
Less Than	`X < Y`	Returns `TRUE` if `X` is less than `Y`.
Less Than or Equal To	`X <= Y`	Returns `TRUE` if `X` is less than or equal to `Y`.
Greater Than	`X > Y`	Returns `TRUE` if `X` is greater than `Y`.
Greater Than or Equal To	`X >= Y`	Returns `TRUE` if `X` is greater than or equal to `Y`.
Equal	`X = Y`	Returns `TRUE` if `X` is equal to `Y`.
Not Equal	`X != Y` `X <> Y`	Returns `TRUE` if `X` is not equal to `Y`.
`BETWEEN`	`X [NOT] BETWEEN [SYMMETRIC] Y AND Z`	Returns `TRUE` if `X` is within the given range of `Y` and `Z`. The `SYMMETRIC` keyword allows `Y` and `Z` to be in any order, rather than `Y` being the lower bound and `Z` being the upper bound.
`LIKE`	`X [NOT] LIKE Y [ESCAPE Z]`	Returns `TRUE` if the string in the `X` operand matches the pattern specified by the `Y` operand. Expressions can contain the following special characters: A percent sign (`%`): Matches any number of characters. An underscore (`_`): Matches a single character. To escape a special character, use the `ESCAPE` keyword followed by the escape character to use. For example `'text' LIKE '!_ext' ESCAPE '!'` returns `FALSE` because the `_` character is escaped by the exclamation mark (`!`).
`IN`		See `IN` Operator

`EXISTS` Operator

The EXISTS operator returns TRUE if a given subquery returns one or more records.

The NOT keyword inverts the result.

Syntax

The EXISTS operator supports the following syntax:

sql
[NOT] EXISTS (<query>)

`IS` Operator

The IS operator returns TRUE or FALSE for a given condition. This operator never returns NULL.

The NOT keyword inverts the result.

Syntax	Description
`X IS [NOT] NULL`	Returns `TRUE` if the `X` operand evaluates to `NULL`, and returns `FALSE` otherwise.
`X IS [NOT] TRUE`	Returns `TRUE` if the `X` operand evaluates to `TRUE`. Returns `FALSE` otherwise.
`X IS [NOT] FALSE`	Returns `TRUE` if the `X` operand evaluates to `FALSE`. Returns `FALSE` otherwise.

`IN` Operator

The IN operator allows you to check for a certain value in a given set of values, and returns TRUE if an equal value is found.

The NOT keyword inverts the result.

note

At the moment, Hazelcast does not support subqueries in IN clauses. For example, you cannot do SELECT column_names FROM table_name WHERE column_name IN (SELECT STATEMENT);

Syntax

The IN operator supports the following syntax:

sql
<search_value> [NOT] IN (<value_set>)

`UNION` and `UNION ALL` Operators

The UNION operator is used to combine the result set of two or more SELECT statements, excluding any duplicate values.

The UNION ALL operator is used to combine the result set of two or more SELECT statements, including any duplicate values.

note

UNION ALL typically performs much better, because duplicate elimination is an expensive operation. Use UNION only if you actually need to remove duplicates.

Syntax

The UNION and UNION ALL Operators support the following syntax:

sql
<query> UNION [ALL] <query>

Mathematical Operators

Mathematical operators perform operations on numbers.

Operator Name	Syntax
Addition	`X + Y`
Subtraction	`X - Y`
Multiplication	`X * Y`
Division	`X / Y`

Conditional Expressions

Conditional expressions allow you to evaluate only certain output values, depending on given conditions.

CASE

The CASE expression evaluates the condition of each WHEN clause and returns the first result where the condition is TRUE. If all conditions are FALSE or NULL, the result of the ELSE clause is returned.

Each condition must be a boolean expression.

Syntax

The CASE expression has two forms:

sql
CASE value
  WHEN value1 THEN result1
  WHEN value2 THEN result2
  ...
  ELSE elseResult
END

This form for returns result1, when value = value1, result2 when value = value2 and elseResult, if value isn't equal to any of the values in the WHEN clause.

sql
CASE
  WHEN condition1 THEN result1
  WHEN condition2 THEN result2
  ...
  ELSE elseResult
END

This form returns result1 when condition1 is TRUE, result2 when condition2 is TRUE and elseResult, if no condition evaluated to TRUE.

NULLIF

The NULLIF expression returns NULL if the two operands are equal and returns the first operand, if operands are not equal. The data type of the returned NULL value is the same as the X expression.

Syntax

sql
NULLIF(X, Y)

Examples

sql
sql> SELECT NULLIF('foo', 'bar');
+--------------------+
|EXPR$0              |
+--------------------+
|foo                 |
+--------------------+
1 row(s) selected
sql> SELECT NULLIF('foo', 'foo');
+--------------------+
|EXPR$0              |
+--------------------+
|NULL                |
+--------------------+

COALESCE

The COALESCE function returns the first non-null operand. If all operands are null, it returns NULL. Arguments to the right of the first non-null argument are not evaluated.

Syntax

sql
COALESCE(X, Y, Z, ...)

Aggregate Functions

Aggregate functions perform calculations such as returning the mean of all data in a particular row.

Function :: Returns	Description
`COUNT(*) :: BIGINT`	Calculates the number of input rows.
`COUNT(field) :: BIGINT`	Calculates the number of input rows in which the field is not null.
`COUNT(DISTINCT field) :: BIGINT`	Calculates the number of distinct values of the given field (ignores the `NULL` value).
`SUM(TINYINT \| SMALLINT \| INT) :: BIGINT, SUM(BIGINT \| DECIMAL) :: DECIMAL, SUM(REAL) :: REAL, SUM(DOUBLE) :: DOUBLE`	Calculates the sum of the non-null input values.
`AVG(DECIMAL) :: DECIMAL, AVG(DOUBLE) :: DOUBLE`	Calculates the mean of all the non-null input values.
`MIN(any) :: same type as the input`	Calculates the minimum of the non-null input values. Applicable also to `OBJECT` type, if the underlying value is `java.lang.Comparable`.
`MAX(any) :: same type as the input`	Calculates the maximum of the non-null input values. Applicable also to `OBJECT` type, if the underlying value is `java.lang.Comparable`.

note

You can use DISTINCT keyword with all aggregate functions. It causes that before calculating the aggregate, duplicates are removed from the set of input values. For example, SUM(DISTINCT) applied to input values 1, 1, 2 will produce 3, because it will add the 1 only once.

For examples of how to use aggregate functions, see the SELECT statement documentation.

Conversion Functions

Conversion functions allow you to convert the result type of one expression to another explicit type.

Function	Description	Example	Result
`CAST(expression AS data_type)`	Converts the result type of `expression` to `data_type`.	`SELECT CAST('2020-06-02 17:00:53.110' AS TIMESTAMP)`	`2020-06-03T00:00:53.11`

Date and Time Functions

Function Description Example Result

Function	Description	Example	Result
`+EXTRACT(<element> FROM <temporal_value>) DOUBLE+`	Returns the `element` date part from the `date` expression. Supported elements: `MILLENIUM`, `CENTURY`, `DECADE`, `YEAR`, `ISOYEAR`, `QUARTER`, `MONTH`, `WEEK`, `DAY`, `DOW`, `ISODOW`, `DOY`, `HOUR`, `MINUTE`, `SECOND`, `MILLISECOND`, `MICROSECOND`, `EPOCH` Supported temporal types: `DATE`, `TIME`, `TIMESTAMP`, `TIMESTAMP WITH TIME ZONE`	`EXTRACT(WEEK FROM date'2017-06-15')`	24
`+TO_TIMESTAMP_TZ(BIGINT) TIMESTAMP WITH TIME ZONE+`	Converts a `BIGINT` value to `TIMESTAMP_WITH_TIMEZONE`. The value is interpreted as number of seconds, milliseconds, microseconds or nanoseconds since the epoch, that is since 1970-01-01 00:00 UTC. The actual time unit of the conversion is determined by the magnitude of the input value: less than the number of milliseconds in a year: seconds less than the number of microseconds in a year: milliseconds less than the number of nanoseconds in a year: microseconds otherwise: nanoseconds This logic causes that any time value between years 1971 up to year 2968 is converted using the correct time unit. Negative values are always converted as seconds.	`SELECT TO_TIMESTAMP_TZ(v) FROM TABLE (generate_series(1,3))`	1970-01-01T03:00:01+03:00 1970-01-01T03:00:02+03:00 1970-01-01T03:00:03+03:00
`TO_EPOCH_MILLIS(TIMESTAMP_WITH_TIMEZONE)`	Converts `TIMESTAMP_WITH_TIMEZONE` values to an `EPOCH` value in milliseconds.	`SELECT TO_EPOCH_MILLIS(date'2022-02-22')`	1645484400000

`+EXTRACT(<element> FROM <temporal_value>): DOUBLE+`

Returns the element date part from the date expression.

Supported elements: MILLENIUM, CENTURY, DECADE, YEAR, ISOYEAR, QUARTER, MONTH, WEEK, DAY, DOW, ISODOW, DOY, HOUR, MINUTE, SECOND, MILLISECOND, MICROSECOND, EPOCH

Supported temporal types: DATE, TIME, TIMESTAMP, TIMESTAMP WITH TIME ZONE

EXTRACT(WEEK FROM date'2017-06-15')

`+TO_TIMESTAMP_TZ(BIGINT): TIMESTAMP WITH TIME ZONE+`

Converts a BIGINT value to TIMESTAMP_WITH_TIMEZONE. The value is interpreted as number of seconds, milliseconds, microseconds or nanoseconds since the epoch, that is since 1970-01-01 00:00 UTC.

The actual time unit of the conversion is determined by the magnitude of the input value:

less than the number of milliseconds in a year: seconds
less than the number of microseconds in a year: milliseconds
less than the number of nanoseconds in a year: microseconds
otherwise: nanoseconds

This logic causes that any time value between years 1971 up to year 2968 is converted using the correct time unit. Negative values are always converted as seconds.

SELECT TO_TIMESTAMP_TZ(v) FROM TABLE (generate_series(1,3))

1970-01-01T03:00:01+03:00

1970-01-01T03:00:02+03:00

1970-01-01T03:00:03+03:00

TO_EPOCH_MILLIS(TIMESTAMP_WITH_TIMEZONE) Converts TIMESTAMP_WITH_TIMEZONE values to an EPOCH value in milliseconds. SELECT TO_EPOCH_MILLIS(date'2022-02-22') 1645484400000

File Table Functions

To execute an ad-hoc query against data in files you can use one of the following table functions:

csv_file
json_flat_file
avro_file
parquet_file

File table functions create a temporary mapping to a file, which is valid for the duration of the query. These functions accept the same options as those available for the file connector.

To configure the temporary mapping, you can use either positional arguments or named arguments:

Positional arguments in a file table function

sql
SELECT * FROM TABLE(
  CSV_FILE('/path/to/directory', '*.csv', MAP['key', 'value'])
);

Named arguments in a file table function

sql
SELECT * FROM TABLE(
  CSV_FILE(path => '/path/to/directory', options => MAP['key', 'value'])
);

JSON Functions

Hazelcast supports the following functions, which can retrieve JSON data.

JSON_QUERY

The JSON_QUERY() function extracts a JSON value from a JSON document or a JSON-formatted string that matches a given JsonPath expression.

Syntax

JSON_QUERY(jsonArg:{VARCHAR | JSON}, jsonPath:VARCHAR [<wrapperBehavior>] [<onClauseArg> ON ERROR] [<onClauseArg> ON EMPTY])` :: JSON

jsonArg: JSON value or a JSON-formatted string.
jsonPath: A JsonPath expression that identifies the data that you want to get from the jsonArg parameter.
wrapperBehavior: What to do with return results.
- WITHOUT [ARRAY] WRAPPER (default): Up to one matched value is returned without wrapping in an array. Fails, if multiple values match.
- WITH [CONDITIONAL] [ARRAY] WRAPPER: Returns a single match directly without wrapping. If there are multiple matches, they are returned as a JSON array.
- WITH UNCONDITIONAL [ARRAY] WRAPPER: Always wrap matched values in a JSON array.
onClauseArg: A value to return in case of the given ON condition:
- EMPTY ARRAY
- EMPTY OBJECT
- ERROR
- NULL

Examples

JSON-formatted string

sql
SELECT
  JSON_QUERY('{"company" : {"employees" : [{"id" : "1"}]}}', '$.company.employees[0]');

-- Result
-- {"id":"1"}

WITH CONDITIONAL WRAPPER

sql
SELECT
  JSON_QUERY('[1,2,3]', '$[*]?(@ > 1)' WITH CONDITIONAL ARRAY WRAPPER);

-- Result
-- [2,3]

WITHOUT WRAPPER

sql
SELECT
  JSON_QUERY('[1,2,3]', '$[*]?(@ > 1)' WITHOUT ARRAY WRAPPER);

-- Result
-- This example throws an error because you cannot return multiple values without an array wrapper.

WITH UNCONDITIONAL WRAPPER

sql
SELECT
  JSON_QUERY('[1,"rainbow",3]', '$[1]' WITH UNCONDITIONAL ARRAY WRAPPER);

-- Result
-- ["rainbow"]

JSON_VALUE

The JSON_VALUE() function extracts a primitive value, such as a string, number, or boolean that matches a given JsonPath expression. This function returns NULL if a non-primitive value is matched, unless the ON ERROR behavior is changed.

Syntax

JSON_VALUE(jsonArg:{VARCHAR | JSON}, jsonPath:VARCHAR [RETURNING dataType] [<onClauseArg> ON ERROR] [<onClauseArg> ON EMPTY])` :: VARCHAR

jsonArg: JSON value or a JSON-formatted string
jsonPath: A JsonPath expression that identifies the data that you want to get from the jsonArg parameter.
RETURNING: Converts the result to the dataType (VARCHAR by default). If the value cannot be converted to the target type, throws an error.
onClauseArg: What to return in case of the ON condition:
- DEFAULT <literal | column | parameter>
- ERROR
- NULL

Examples

JSON-formatted string

sql
SELECT
  JSON_VALUE('{"company" : {"employees" : [{"id" : "1","name":"jake"}]}}', '$.company.employees[0].id');

-- Returns
-- 1 (as a VARCHAR)

JSON_ARRAY

The JSON_ARRAY() function returns a JSON array from a list of input data.

Syntax

JSON_ARRAY([columnOrParameterOrLiteral:ANY], [...more columns/parameters/literals:ANY] [{ABSENT|NULL} ON NULL]) :: JSON

columnOrParameterOrLiteral: A list of input data.
ON NULL: What to do with null values:
- ABSENT ON NULL (default): Do not include NULL values in the array.
- NULL ON NULL: Include NULL values in the array.

Examples

sql
SELECT
  JSON_ARRAY(1, null, 3);

-- Result
-- [1,3]

JSON_OBJECT

The JSON_OBJECT() function returns a JSON object from the given key/value pairs.

Syntax

JSON_OBJECT([key : value] [, ...] [{ABSENT|NULL} ON NULL]) :: JSON

JSON_OBJECT([[KEY] key VALUE value] [{ABSENT|NULL} ON NULL]) :: JSON

key: A name for the key, must be a VARCHAR
value: A value for the key, can be any type.
ON NULL: What to do with NULL values.
- NULL ON NULL (default): Include `NULL values in the array.
- ABSENT ON NULL: Do not include NULL values in the array.

Examples

sql
SELECT JSON_OBJECT(KEY 'id' VALUE 1, 'name' VALUE null ABSENT ON NULL)

-- Result
-- {"id": 1}

sql
SELECT JSON_OBJECT('id': 1, 'name': 'jake')

-- Result
-- {"id": 1, "name":"jake"}

JSON_ARRAYAGG

The JSON_ARRAYAGG() returns a JSON array containing an element for each value in a given set of SQL values. It takes as its input a column of SQL expressions, converts each expression to a JSON value, and returns a single JSON array that contains those JSON values.

Syntax

JSON_ARRAY(value [ORDER BY value {ASC|DESC}] [{ABSENT|NULL} ON NULL]) :: JSON

value: A value for the key, can be any type.
ON NULL: What to do with null values:
- ABSENT ON NULL (default): Do not include NULL values in the array.
- NULL ON NULL: Include NULL values in the array.

Examples

Assuming you have the following table:

name, number
--------
Alice, 1
Bob, 2
Alice, 3
Bob, 6
null, 7

sql
SELECT name,  JSON_ARRAYAGG(number ORDER BY number ASC) arr_no
FROM test GROUP BY name

-- Result
-- name, arr_no
-- ------------
-- Alice, [1, 3]
-- Bob, [2, 6]
-- null, [7]

JSON_OBJECTAGG

The JSON_OBJECTAGG() function constructs an object member for each key-value pair and returns a single JSON object that contains those object members. It takes as its input a property key-value pair. Typically, the property key, the property value, or both are columns of SQL expressions.

Syntax

JSON_OBJECTAGG([key : value] [, ...] [{ABSENT|NULL} ON NULL]) :: JSON

JSON_OBJECTAGG([[KEY] key VALUE value] [{ABSENT|NULL} ON NULL]) :: JSON

key: A name for the key, must be a VARCHAR
value: A value for the key, can be any type.
ON NULL: What to do with NULL values.
- NULL ON NULL (default): Include `NULL values in the array.
- ABSENT ON NULL: Do not include NULL values in the array.

Examples

sql
SELECT JSON_OBJECTAGG(KEY 'name' VALUE age) "Names"
  FROM employees
  WHERE age <= 30;

--Result
--{"Mary":28,"John":29,"Jake":27}

Mathematical Functions

Function	Description	Example	Result
`ABS(number)`	Absolute value of the argument	`ABS(-5)`	`5`
`CBRT(number)`	Returns the cube root of the input	`CBRT(343)`	`7`
`CEIL(number)`	Returns the nearest integer greater than or equal to argument	`CEIL(25.3)`	`26`
`DEGREES(DOUBLE)`	Converts radians to degrees	`DEGREES(0.67)`	`38.38817227376516`
`EXP(number)`	Exponential	`EXP(2.5)`	`12.182493960703473`
`FLOOR(number)`	Returns the nearest integer less than or equal to argument	`FLOOR(25.3)`	`25`
`LN(number)`	Natural logarithm	`LN(2.5)`	`0.9162907318741551`
`LOG10(number)`	Base 10 logarithm	`LOG(2.5)`	`0.3979400086720376`
`MOD(x:number, y:number)`	Returns the remainder of x / y	`MOD(20,3)`	`2`
`POWER(x:number, y:number)`	Returns x to the power of y	`POWER(20,3)`	`8000`
`RADIANS(DOUBLE)`	Converts degrees to radians	`RADIANS(38.39)`	`0.6700318998406232`
`RAND`	Random value in the range [0.0; 1.0)	`RAND()`	`0.6324099982812553`
`RAND(number)`	Random value in the range [0.0; 1.0) using the given seed	`RAND(10)`	`0.7304302967434272`
`ROUND(number)`	Rounds to an integer	`ROUND(34.5678)`	`35`
`ROUND(number, s:integer)`	Rounds to `s` decimal places	`ROUND(34.5678, 2)`	`34.57`
`SIGN(number)`	Returns -1, 0 or 1 for negative, zero or positive argument, respectively	`SIGN(-25)`	`-1`
`SQUARE(number)`	Squares the input	`SQUARE(2)`	`4`
`SQRT(number)`	Returns the square root of the input	`SQRT(4)`	`2`
`TRUNCATE(number)`	Truncates to an integer	`TRUNC(34.5678)`	`34`
`TRUNCATE(number, s:integer)`	Truncates to `s` decimal places	`TRUNC(34.5678, 2)`	`34.56`

String Functions

Function	Description	Example	Result
`string \|\| string`	Concatenates two strings	`'John' \|\| ' ' \|\| 'Doe'`	`John Doe`
`ASCII(string)`	Returns the ASCII code of the first character of the argument	`ASCII('a')`	`97`
`BTRIM(string)`	Equivalent to `TRIM(BOTH ' ' FROM string)`
`CONCAT_WS(separator_string, string1, string2, …)`	Returns a string that consists of the arguments `string1` `separator_string` `string2`	`CONCAT_WS('-', 'John', 'Doe')`	John-Doe
`INITCAP(string)`	Converts the first letter of each word to upper case, and the rest to lower case	`INITCAP('john DOE')`	`John Doe`
`LENGTH(string)`	Length of the string	`LENGTH('John Doe')`	`8`
`LOWER(string)`	Converts the string to lower case	`LOWER('John Doe')`	`john doe`
`LTRIM(string)`	Removes the empty spaces from the left-hand side of `string`. This function is equivalent to `TRIM(LEADING ' ' FROM string)`	`RTRIM( John Doe')`	`John Doe`
`POSITION(substring IN string [FROM position])`	Returns the position of the first occurrence of `substring` in `string`	`POSITION('Doe' IN 'John Doe')`	`5`
`REPLACE(substring, old_string, new_string)`	Replaces all occurrences of `substring` in `old_string` with `new_string`	`REPLACE('John', 'John Doe', 'Jane')`	`Jane Doe`
`RTRIM(string)`	Removes the empty spaces from the right-hand side of `string`. This function is equivalent to `TRIM(TRAILING ' ' FROM string)`	`RTRIM(John Doe ')`	`John Doe`
`SUBSTRING(string FROM integer)`	Extracts a substring starting with the given position	`SUBSTRING('John Doe' FROM 6)`	`Doe`
`SUBSTRING(string FROM integer FOR integer)`	Extracts a substring starting with the given position for the given length	`SUBSTRING('John Doe' FROM 1 FOR 4)`	`John`
`TRIM([LEADING\|TRAILING\|BOTH] [characters FROM] string)`	Removes `characters` (a space by default) from the start/end/both ends of the string	`TRIM(BOTH '[]' FROM '[John Doe]')`	`John Doe`
`TRIM(characters FROM string)`	Equivalent to `TRIM(BOTH characters FROM string)`	`TRIM('[]' FROM '[John Doe]')`	`John Doe`
`TRIM(string)`	Equivalent to `TRIM(BOTH ' ' FROM string)`	`TRIM(' John Doe ')`	`John Doe`
`UPPER(string)`	Converts a string to upper case	`UPPER('John Doe')`	`JOHN DOE`

Table-Valued Functions

Table-valued functions return tables of batch or streaming sources that you can use in SQL statements.

Function	Description
`generate_series(start_number, end_number)`	Returns a table that contains a series of numbers, starting from the `start_number` argument and ending with the `stop_number` arguments.
`generate_stream(numbers_per_second)`	Returns a table that contains a stream of numbers, starting from 0 at a rate of `numbers_per_second` every second, without an upper bound

To use a table-valued function in FROM clause, you must wrap it in a TABLE keyword. For example:

sql
SELECT *
FROM TABLE(generate_series(1, 3));

Trigonometric Functions

Function	Description
`ACOS(double)`	Inverse cosine
`ASIN(double)`	Inverse sine
`ATAN(double)`	Inverse tangent
`ATAN2(x:number, y:number)`	Arc tangent
`COS(double)`	Cosine
`COT(double)`	Cotangent
`SIN(double)`	Sine
`TAN(double)`	Tangent

Windowing Table-Valued Functions

Windowing functions assign input records from the input table into windows. Their output contains all the input columns, with two added columns: window_start and window_end. You can use the added columns in the GROUP BY expression when doing streaming aggregation.

For a guide about streaming windowed aggregations in SQL, see Stream Processing in SQL.

Function	Description
`TUMBLE(TABLE(input),DESCRIPTOR(time_col),window_size)`	Assigns input records to tumbling windows.
`HOP(TABLE(input),DESCRIPTOR(time_col),window_size, slide_step)`	Assigns input records to hopping windows.

Operators​

Logical Operators​

Comparison Operators​

EXISTS Operator​

IS Operator​

IN Operator​

UNION and UNION ALL Operators​

Mathematical Operators​

Conditional Expressions​

CASE​

NULLIF​

COALESCE​

Aggregate Functions​

Conversion Functions​

Date and Time Functions​

File Table Functions​

JSON Functions​

JSON_QUERY​

JSON_VALUE​

JSON_ARRAY​

JSON_OBJECT​

JSON_ARRAYAGG​

JSON_OBJECTAGG​

Mathematical Functions​

String Functions​

Table-Valued Functions​

Trigonometric Functions​

Windowing Table-Valued Functions​

Operators

Logical Operators

Comparison Operators

`EXISTS` Operator

`IS` Operator

`IN` Operator

`UNION` and `UNION ALL` Operators

Mathematical Operators

Conditional Expressions

CASE

NULLIF

COALESCE

Aggregate Functions

Conversion Functions

Date and Time Functions

File Table Functions

JSON Functions

JSON_QUERY

JSON_VALUE

JSON_ARRAY

JSON_OBJECT

JSON_ARRAYAGG

JSON_OBJECTAGG

Mathematical Functions

String Functions

Table-Valued Functions

Trigonometric Functions

Windowing Table-Valued Functions